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Results for “Merck Gene Index”

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7 recordsLinked to original sources
PubMed1999

The Merck Gene Index project.

The Merck Gene Index project (MGIP) fills an important niche in the Human Genome Project by directly identifying genes through sequences of their transcripts and placing in the public domain a set of EST sequences and associated clones for the uniquely expressed human genes. The MGIP promotes the unrestricted exchange of human genomic data, and facilitates progress in biomedical research by reducing duplication of efforts, speeding the identification of disease-related genes and furthering our understanding of disease processes. The project is stimulating biological research, which in turn is the driving force in drug discovery today.

Journal Article↗
PubMed1998

The Merck Gene Index browser: an extensible data integration system for gene finding, gene characterization and EST data mining.

MOTIVATION: To make effective use of the vast amounts of expressed sequence tag (EST) sequence data generated by the Merck-sponsored EST project and other similar efforts, sequences must be organized into gene classes, and scientists must be able to 'mine' the gene class data in the context of related genomic data. RESULTS: This paper presents the Merck Gene Index browser, an easily extensible, World Wide Web-based system for mining the Merck Gene Index (MGI) and related genomic data. The MGI is a non-redundant set of clones and sequences, each representing a distinct gene, constructed from all high-quality 3' EST sequences generated by the Merck-sponsored EST project. The MGI browser integrates data from a variety of sources and storage formats, both local and remote, using an eclectic integration strategy, including a federation of relational databases, a local data warehouse and simple hypertext links. Data currently integrated include: LENS cDNA clone and EST data, dbEST protein and non-EST nucleic acid similarity data, WashU sequence chromatograms. Entrez sequence and Medline entries, and UniGene gene clusters. Flatfile sequence data are accessed using the Bioapps server, an internally developed client-server system that supports generic sequence analysis applications. Browser data are retrieved and formatted by means of the Bioinformatics Data Integration Toolkit (B-DIT), a new suite of Perl routines.

Abstracting and Indexing↗
PubMed2001

Genome analysis with gene-indexing databases.

The recent release of the draft sequence and the eventual completion of the human genome present the scientific community with a rich source of data to mine. Yet, these data are content poor in the absence of additional correlative information. Expressed sequence tag (EST) datasets and their associated gene indices have existed for many years, and represent the first attempt at understanding the complexity of the genome. These datasets remain extremely important as information sources and, in particular, as tools for analyzing the completed genomes. Here, we discuss the nature of ESTs and their associated tools and gene-indexing databases. In particular, we will compare three EST gene indices (UNIGENE, Merck Gene Index Version 2.0 and Doubletwist CAT), discuss how these gene indices are applied for both genome analysis and drug discovery, and demonstrate their importance as a complementary dataset to the annotated human genome.

Databases, Factual↗
PubMed2003

Patenting human genes: when economic interests trump logic and ethics.

To date, over 5,000 applications have been filed with United States Patent Office for patents on human genes. More than 1,500 of these applications have been granted. Other jurisdictions are experiencing a similar rush to mine and protect genomic gold. This paper argues that although many jurisdictions allow the patenting of human genes, this is ethically indefensible and amounts to an unjustified appropriation of a general human heritage. Economic and legal arguments in favour of patenting are considered and rejected. Reference is made to the Wellcome Trust Consortium's initiative and the Merck Gene Index Project, which place patented genetic information into the public domain.

Biotechnology↗
PubMed1996

Toward the development of a gene index to the human genome: an assessment of the nature of high-throughput EST sequence data.

A rigorous analysis of the Merck-sponsored EST data with respect to known gene sequences increases the utility of the data set and helps refine methods for building a gene index. A highly curated human transcript data base was used as a reference data set of known genes. A detailed analysis of EST sequences derived from known genes was performed to assess the accuracy of EST sequence annotation. The EST data was screened to remove low-quality and low-complexity sequences. A set of high-quality ESTs similar to the transcript data base was identified using BLAST; this subset of ESTs was compared with the set of known genes using the Smith-Waterman algorithm. Error rates of several types were assessed based on a flexible match criterion defining sequence identity. The rate of lane-tracking errors is very low, approximately 0.5%. Insert size data is accurate within approximately 20%. Reversed clone and internal priming error rates are approximately 5% and 2.5%, respectively, contributing to the incorrect identification of reads as 3' ends of genes. Follow-up investigation reveals that a significant number of clones, miscategorized as reversed, represent overlapping genes on the opposite strand of entries in the transcript data base. Relevance of these results to the creation of a high-quality index to the human genome capable of supporting diverse genomic investigations is discussed.

Algorithms↗